A global blockchain for recording high rates of COVID-19 vaccinations.

Blockchain has been recently proposed to securely record vaccinations against COVID-19 and manage their verification. However, existing solutions may not fully meet the requirements of a global vaccination management system. These requirements include the scalability required to support a global vaccination campaign, like one against COVID-19, and the capability to facilitate the interoperation between the independent health administrations of different countries. Moreover, access to global statistics can help to control securing community health and provide continuity of care for individuals during a pandemic. In this paper, we propose GEOS, a blockchain-based vaccination management system designed to address the challenges faced by the global vaccination campaign against COVID-19. GEOS offers interoperability between vaccination information systems at both domestic and international levels, supporting high vaccination rates and extensive coverage for the global population. To provide those features, GEOS uses a two-layer blockchain architecture, a simplified byzantine-tolerant consensus algorithm, and the Boneh-Lynn-Shacham signature scheme. We analyze the scalability of GEOS by examining transaction rate and confirmation times, considering factors such as the number of validators, communication overhead, and block size within the blockchain network. Our findings demonstrate the effectiveness of GEOS in managing COVID-19 vaccination records and statistical data for 236 countries, encompassing crucial information such as daily vaccination rates for highly populous nations and the global vaccination demand, as identified by the World Health Organization.

Increased Rates of Hospitalized Children with Type 1 and Type 2 Diabetes Mellitus in Central Brooklyn during the COVID-19 Pandemic.

Following reports of increased new-onset diabetes and worse severity of DKA for children with diabetes following SARS-CoV-2 infection, we studied hospitalization rates for children with type 1 diabetes (T1DM) and type 2 diabetes (T2DM) in our center during the citywide shutdown. Methods. We conducted a retrospective chart review of children admitted to our two hospitals from January 1, 2018, to December 31, 2020. We included ICD-10 codes for diabetic ketoacidosis (DKA), hyperglycemic hyperosmolar syndrome (HHS), and hyperglycemia only. Results. We included 132 patients with 214 hospitalizations: 157 T1DM, 41 T2DM, and 16 others (14 steroid induced, 2 MODY). Overall admissions rates for patients with all types of diabetes were 3.08% in 2018 to 3.54% in 2019 (p = 0.0120) and 4.73% in 2020 (p = 0.0772). Although there was no increase of T1DM admissions across all 3 years, T2DM admission rates increased from 0.29% to 1.47% (p = 0.0056). Newly diagnosed T1DM rates increased from 0.34% in 2018 to 1.28% (p = 0.002) in 2020, and new-onset T2DM rates also increased from 0.14% in 2018 to 0.9% in 2020 (p = 0.0012). Rates of new-onset diabetes presenting with DKA increased from 0.24% in 2018 to 0.96% in 2020 (p = 0.0014). HHS increased from 0.1% in 2018 to 0.45% in 2020 (p = 0.044). The severity of DKA in newly diagnosed was unaffected (p = 0.1582). Only 3 patients tested positive for SARS-CoV-2 infection by PCR. Conclusion. Our urban medical center is located in Central Brooklyn and serves a majority who are Black. This is the first study investigating pediatric diabetes cases admitted to Brooklyn during the first wave of the pandemic. Despite the overall pediatric admissions declining in 2020 due to the citywide shutdown, overall hospitalization rates in children with T2DM and in new-onset T1DM and T2DM increased, which is not directly associated with active SARS-CoV-2 infection. More studies are needed to elucidate the reason for this observed increase in hospitalization rates.

Modified body mass index z-scores in children in New York City during the COVID-19 pandemic.

OBJECTIVES: Determine whether the negative impact of the COVID-19 pandemic on weight gain trajectories among children attending well-child visits in New York City persisted after the public health restrictions were reduced. STUDY DESIGN: Multicenter retrospective chart review study of 7150 children aged 3-19 years seen for well-child care between 1 January 2018 and 4 December 2021 in the NYC Health and Hospitals system. Primary outcome was the difference in annual change of modified body mass index z-score (mBMIz) between the pre-pandemic and early- and late-pandemic periods. The mBMIz allows for tracking of a greater range of BMI values than the traditional BMI z-score. The secondary outcome was odds of overweight, obesity, or severe obesity. Multivariable analyses were conducted with each outcome as the dependent variable, and year, age category, sex, race/ethnicity, insurance status, NYC borough, and baseline weight category as independent variables. RESULTS: The difference in annual mBMIz change for pre-pandemic to early-pandemic = 0.18 (95% confidence interval [CI]: 0.15, 0.20) and for pre-pandemic to late-pandemic = 0.04 (95% CI: 0.01, 0.06). There was a statistically significant interaction between period and baseline weight category. Those with severe obesity at baseline had the greatest mBMIz increase during both pandemic periods and those with underweight at baseline had the lowest mBMIz increase during both pandemic periods. CONCLUSION: In NYC, the worsening mBMIz trajectories for children associated with COVID-19 restrictions did not reverse by 2021. Decisions about continuing restrictions, such as school closures, should carefully weigh the negative health impact of these policies.

Reducing COVID-19 Cases and Deaths by Applying Blockchain in Vaccination Rollout Management.

Goal: Because a fast vaccination rollout against coronavirus disease 2019 (COVID-19) is critical to restore daily life and avoid virus mutations, it is tempting to have a relaxed vaccination-administration management system. However, a rigorous management system can support the enforcement of preventive measures, and in turn, reduce incidence and deaths. Here, we model a trustable and reliable management system based on blockchain for vaccine distribution by extending the Susceptible-Exposed-Infected-Recovery (SEIR) model. The model includes prevention measures such as mask-wearing, social distancing, vaccination rate, and vaccination efficiency. It also considers negative social behavior, such as violations of social distance and attempts of using illegitimate vaccination proofs. By evaluating the model, we show that the proposed system can reduce up to 2.5 million cases and half a million deaths in the most demanding scenarios.

Correlation of subway turnstile entries and COVID-19 incidence and deaths in New York City.

In this paper, we show a strong correlation between turnstile entries data of the New York City (NYC) subway provided by NYC Metropolitan Transport Authority and COVID-19 deaths and cases reported by the NYC Department of Health from March to May 2020. This correlation is obtained through linear regression and confirmed by the prediction of the number of deaths by a Long Short-Term Memory neural network. The correlation is more significant after considering incubation and symptomatic phases of this disease as experienced by people who died from it. We extend the analysis to each individual NYC borough. We also estimate the dates when the number of COVID-19 deaths and cases would approach zero by using the Auto-Regressive Integrated Moving Average model on the reported deaths and cases. We also backward forecast the dates when the first cases and deaths might have occurred.

Survey of Saliva Components and Virus Sensors for Prevention of COVID-19 and Infectious Diseases.

The United States Centers for Disease Control and Prevention considers saliva contact the lead transmission means of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes the coronavirus disease 2019 (COVID-19). Saliva droplets or aerosols expelled by heavy breathing, talking, sneezing, and coughing may carry this virus. People in close distance may be exposed directly or indirectly to these droplets, especially those droplets that fall on surrounding surfaces and people may end up contracting COVID-19 after touching the mucosa tissue on their faces. It is of great interest to quickly and effectively detect the presence of SARS-CoV-2 in an environment, but the existing methods only work in laboratory settings, to the best of our knowledge. However, it may be possible to detect the presence of saliva in the environment and proceed with prevention measures. However, detecting saliva itself has not been documented in the literature. On the other hand, many sensors that detect different organic components in saliva to monitor a person's health and diagnose different diseases that range from diabetes to dental health have been proposed and they may be used to detect the presence of saliva. This paper surveys sensors that detect organic and inorganic components of human saliva. Humidity sensors are also considered in the detection of saliva because a large portion of saliva is water. Moreover, sensors that detect infectious viruses are also included as they may also be embedded into saliva sensors for a confirmation of the virus' presence. A classification of sensors by their working principle and the substance they detect is presented. This comparison lists their specifications, sample size, and sensitivity. Indications of which sensors are portable and suitable for field application are presented. This paper also discusses future research and challenges that must be resolved to realize practical saliva sensors. Such sensors may help minimize the spread of not only COVID-19 but also other infectious diseases.